5 Result(s)
within
Chapter
Junhui Hou
Image Processing and Computer Vision
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Chapter and Conference Paper
DRLFNet: A Dense-Connection Residual Learning Neural Network for Light Field Super Resolution
Light field records both spatial and angular information of light rays. By using light field cameras, 3D scenes can be reconstructed easily for further virtual reality applications. Limited by the sensor size,...
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Chapter and Conference Paper
PUGeo-Net: A Geometry-Centric Network for 3D Point Cloud Upsampling
In this paper, we propose a novel deep neural network based method, called PUGeo-Net, for upsampling 3D point clouds. PUGeo-Net incorporates discrete differential geometry into deep learning elegantly by learn...
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Chapter and Conference Paper
Deep Spatial-Angular Regularization for Compressive Light Field Reconstruction over Coded Apertures
Coded aperture is a promising approach for capturing the 4-D light field (LF), in which the 4-D data are compressively modulated into 2-D coded measurements that are further decoded by reconstruction algorithm...
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Chapter and Conference Paper
Fast Light Field Reconstruction with Deep Coarse-to-Fine Modeling of Spatial-Angular Clues
Densely-sampled light fields (LFs) are beneficial to many applications such as depth inference and post-capture refocusing. However, it is costly and challenging to capture them. In this paper, we propose a le...
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Chapter and Conference Paper
Random Forest with Suppressed Leaves for Hough Voting
Random forest based Hough-voting techniques have been widely used in a variety of computer vision problems. As an ensemble learning method, the voting weights of leaf nodes in random forest play critical role ...
